SOFARPC is a high-performance, high-extensibility, production-level Java RPC framework.

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

Robotics Toolbox for Python

Jupiter是一款性能非常不错的, 轻量级的分布式服务框架

PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks

A high performance, distributed RPC framework.（分布式服务框架XXL-RPC）

Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

A Tutorial on Manipulator Differential Kinematics

Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

A lightweight, high throughput, and low latency RPC framework that supports the RDMA protocol

⚡️ Nepxion Thunder is a distribution RPC framework based on Netty + Hessian + Kafka + ActiveMQ + Tibco + Zookeeper + Redis + Spring Web MVC + Spring Boot + Docker 多协议、多组件、多序列化的分布式RPC调用框架

caucho hessian2 implementation in Go for [apache/dubbo-go](https://github.com/apache/dubbo-go) which is compatible with [dubbo-hessian-lite](https://github.com/apache/dubbo-hessian-lite)

js hessian binary web service protocol, support communicate with java

A high-performance, open-source java RPC framework.

A local reactive cache for Java and Android. Now, it supports heap memory、off-heap memory and disk cache.

An internal improved version of Hessian3/4 powered by Ant Group CO., Ltd.

Easy-to-use AdaHessian optimizer (PyTorch)

提供Java、Web、各种类库增强，尽量避免996 。目前已支持 feign, redis, datasource, shiro, httpclient, rest, codec, serialize, jersey, 多种ssh client 等

This Repository contains Solutions to the Quizes & Lab Assignments of the Mathematics for Machine Learning Specialization offered by Imperial College of London on Coursera taught by David Dye, Samuel J. Cooper, A. Freddie Page, Marc Peter Deisenroth